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Classification of hand posture from electrocorticographic signals recorded during varying force conditions

Degenhart, AD and Collinger, JL and Vinjamuri, R and Kelly, JW and Tyler-Kabara, EC and Wang, W (2011) Classification of hand posture from electrocorticographic signals recorded during varying force conditions. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 5782 - 5785. ISSN 1557-170X

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Abstract

In the presented work, standard and high-density electrocorticographic (ECoG) electrodes were used to record cortical field potentials in three human subjects during a hand posture task requiring the application of specific levels of force during grasping. We show two-class classification accuracies of up to 80% are obtained when classifying between two-finger pinch and whole-hand grasp hand postures despite differences in applied force levels across trials. Furthermore, we show that a four-class classification accuracy of 50% is achieved when predicting both hand posture and force level during a two-force, two-hand-posture grasping task, with hand posture most reliably predicted during high-force trials. These results suggest that the application of force plays a significant role in ECoG signal modulation observed during motor tasks, emphasizing the potential for electrocorticography to serve as a source of control signals for dexterous neuroprosthetic devices. © 2011 IEEE.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Degenhart, ADadd19@pitt.eduADD19
Collinger, JLcollinger@pitt.eduCOLLINGR
Vinjamuri, R
Kelly, JW
Tyler-Kabara, ECtylerk@pitt.eduTYLERK
Wang, Wwangwei3@pitt.eduWANGWEI3
Centers: Other Centers, Institutes, Offices, or Units > Human Engineering Research Laboratories
Date: 26 December 2011
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Page Range: 5782 - 5785
DOI or Unique Handle: 10.1109/iembs.2011.6091431
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Refereed: No
ISSN: 1557-170X
MeSH Headings: Algorithms; Electroencephalography--methods; Hand--physiology; Hand Strength--physiology; Humans; Motor Cortex--physiology; Muscle Contraction--physiology; Muscle Strength--physiology; Muscle, Skeletal--physiology; Physical Exertion--physiology; Posture--physiology; Reproducibility of Results; Sensitivity and Specificity
PubMed ID: 22255654
Date Deposited: 30 Jan 2013 20:58
Last Modified: 05 Oct 2020 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/17129

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